app.py

import streamlit as st
import json
from transformers import AutoTokenizer, AutoModelForSequenceClassification, pipeline

# Initialize Streamlit
st.set_page_config(page_title="PhoBert Q&A ChatBot")
st.markdown(
    """
    <style>
    .reportview-container {
        background: url('Background.png');
        background-size: cover;
    }
    .sidebar .sidebar-content {
        background: url('Background.png');
        background-size: cover;
    }
    .header-logo {
        width: 200px;
        display: block;
        margin-left: auto;
        margin-right: auto;
    }
    </style>
    """,
    unsafe_allow_html=True
)

# Add logo
st.markdown(
    """
    <div>
        <img src="logo.png" class="header-logo">
    </div>
    """,
    unsafe_allow_html=True
)

st.header("PhoBert Q&A ChatBot")

if 'chat_history' not in st.session_state:
    st.session_state['chat_history'] = []

# User input
user_input = st.text_input("Input :", key="input")
submit = st.button("Chat With Bot")

# Load model and tokenizer
model_path = "minhdang14902/PhoBert_Edu"
model = AutoModelForSequenceClassification.from_pretrained(model_path)
tokenizer = AutoTokenizer.from_pretrained(model_path)
chatbot = pipeline("sentiment-analysis", model=model, tokenizer=tokenizer)

# Load intents from file
def load_intents_from_txt(input_filename):
    intents = {}
    with open(input_filename, "r", encoding='utf-8') as f:
        for line in f:
            key, value = line.strip().split(": ", 1)
            intents[key] = json.loads(value)
    return intents

# Load id2label and label2id from file
id2label = {}
with open("id2label.txt", "r", encoding='utf-8') as f:
    for line in f:
        id, label = line.strip().split(": ")
        id2label[int(id)] = label

label2id = {}
with open("label2id.txt", "r", encoding='utf-8') as f:
    for line in f:
        label, id = line.strip().split(": ")
        label2id[label] = int(id)

intents = load_intents_from_txt("intents.txt")

def get_response(user_input):
    st.subheader("The Answer is:")
    st.write(user_input)
    result = chatbot(user_input)[0]
    score = result['score']
    st.write(score)

    if score < 0.001:
        return "Sorry, I can't answer that"
    
    label = label2id[result['label']]
    st.write(label)
    return intents['intents'][label]['responses']

if submit and user_input:
    st.session_state['chat_history'].append(("User", user_input))

    response = get_response(user_input)

    st.subheader("The Response is:")
    message = st.empty()
    result = ""
    for chunk in response:
        result += chunk
        message.markdown(result + "❚ ")
    message.markdown(result)
    st.session_state['chat_history'].append(("Bot", result))

for i, (sender, message) in enumerate(st.session_state['chat_history']):
    if sender == "User":
        st.text_area(f"User {i}:", value=message, height=100, max_chars=None, key=f"user_{i}")
    else:
        st.text_area(f"Bot {i}:", value=message, height=100, max_chars=None, key=f"bot_{i}")